Assessing candidacy for bilateral cochlear implants: A survey of practices in the United States and Canada
Bibliographic record
Abstract
OBJECTIVES: There are currently no agreed-upon criteria to establish candidacy for bilateral cochlear implants (CIs). This study categorized practice patterns for establishing bilateral CI candidacy. METHODS: A postal survey was sent to all practices performing CIs in the United States and Canada. The survey queried centers regarding candidacy criteria for bilateral implantation, testing parameters, definition of 'best aided condition', use of testing in noise, localization, and quality-of-life questionnaires. The survey was resent to non-responding centers 4 weeks after the initial mailing. RESULTS: The overall response rate was 40%. 'Best aided condition' (70%) and hearing in noise (52%) were used to establish bilateral candidacy, while 45% of centers offered bilateral implants to all candidates. The majority of respondents defined 'best aided' as hearing aids only (57% non-exclusive) or CI and hearing aid together (57%). Only 25% considered a CI alone as best aided. Nearly 5% considered no aiding to be the best aided. Sound localization was used by 8% of respondents for candidacy assessment. Reimbursement affected candidacy decision for 45%. There was variability in stimulus levels (60, 50, 45, and 55 dB), signal-to-noise ratios, and speaker orientations used. DISCUSSION: There are no consistent criteria to assess patients for bilateral CIs. This practice variation makes comparing outcomes across centers challenging and leaves open the possibility of having external standards imposed by regulators or payors. Standardization of candidacy assessment is necessary to develop best practices for bilateral cochlear implantation both to optimize patient outcomes and to ensure the continuity of coverage for these services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".